AI-Ready Data Ingestion with Snowflake
A foundational, decision-focused session for architects and data engineers: learn when and what to use, not just how.
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This is Part 1 of a 2-part series. Once you've got the right data in, the next question is what to do with it. Join us on 15 October for Data Transformation Strategies with Snowflake: Choosing the Right Approach for Every Pipeline, using the same decision-framework format, focused on transformations.
Batch loads, Snowpipe, streaming, change data capture, managed connectors, query-in-place - Snowflake gives you many ways to get data in and make it ready for AI workloads. The hard part is knowing which to use, and when.
In this session we cut through the options with a simple, repeatable decision framework that maps any source and latency requirement to the right Snowflake ingestion capability - so data you deliver is fresh, governed and ready to support trusted AI outcomes - including the cases where the smartest move is not to move the data at all.
What you’ll learn
A repeatable framework for matching a source + latency requirement to the right ingestion capability and AI workload requirement
When batch loading makes sense - and when it becomes an anti-pattern
How to choose the right streaming pattern when AI applications need more current data
How ingestion choices influence AI data freshness, reliability and operational cost
When Openflow managed connectors (database CDC or SaaS) are better than building your own pipeline
When not to ingest at all — querying in place and sharing instead of copying (zero-copy integrations) to reduce unnecessary data movement and duplication
What we’ll cover
The ingestion patterns: batch → continuous → streaming
Batch and bulk loading for scheduled, high-volume data
Continuous file ingestion with Snowpipe
Real-time, row-level streaming
Openflow: managed connectors and change data capture
Query-in-place and zero-copy data sharing
The role of ingestion in building a trusted, timely data foundation for AI
A capability selector you can take back to your team
Who should attend
Enterprise and data architects, data-engineering team leads, and data engineers who want a clear mental model for ingestion decisions that supports both analytics and AI use cases — no click-by-click tutorials, just the when and what.
Speakers
